Overview

Dataset statistics

Number of variables40
Number of observations3382812
Missing cells66696504
Missing cells (%)49.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 GiB
Average record size in memory320.0 B

Variable types

Categorical16
Numeric18
Unsupported6

Alerts

id_mutation has a high cardinality: 1447220 distinct values High cardinality
date_mutation has a high cardinality: 365 distinct values High cardinality
adresse_nom_voie has a high cardinality: 478212 distinct values High cardinality
adresse_code_voie has a high cardinality: 15865 distinct values High cardinality
nom_commune has a high cardinality: 30549 distinct values High cardinality
ancien_nom_commune has a high cardinality: 782 distinct values High cardinality
id_parcelle has a high cardinality: 2029416 distinct values High cardinality
ancien_id_parcelle has a high cardinality: 16409 distinct values High cardinality
code_nature_culture_speciale has a high cardinality: 125 distinct values High cardinality
nature_culture_speciale has a high cardinality: 125 distinct values High cardinality
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot4_surface_carrez and 4 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 4 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
valeur_fonciere is highly correlated with lot4_surface_carrezHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot5_surface_carrezHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 2 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with valeur_fonciere and 3 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
nombre_pieces_principales is highly correlated with code_type_localHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot4_surface_carrez and 2 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 2 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
adresse_numero is highly correlated with adresse_suffixeHigh correlation
adresse_suffixe is highly correlated with adresse_numero and 3 other fieldsHigh correlation
code_postal is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
ancien_code_commune is highly correlated with adresse_suffixe and 2 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 2 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 4 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with ancien_code_commune and 4 other fieldsHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
code_nature_culture is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
nature_culture is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
longitude is highly correlated with code_postal and 2 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
valeur_fonciere has 45338 (1.3%) missing values Missing
adresse_numero has 1395778 (41.3%) missing values Missing
adresse_suffixe has 3237253 (95.7%) missing values Missing
ancien_code_commune has 3323835 (98.3%) missing values Missing
ancien_nom_commune has 3323835 (98.3%) missing values Missing
ancien_id_parcelle has 3361806 (99.4%) missing values Missing
numero_volume has 3373972 (99.7%) missing values Missing
lot1_numero has 2313128 (68.4%) missing values Missing
lot1_surface_carrez has 3104314 (91.8%) missing values Missing
lot2_numero has 3161194 (93.4%) missing values Missing
lot2_surface_carrez has 3314519 (98.0%) missing values Missing
lot3_numero has 3346137 (98.9%) missing values Missing
lot3_surface_carrez has 3376163 (99.8%) missing values Missing
lot4_numero has 3370210 (99.6%) missing values Missing
lot4_surface_carrez has 3381060 (99.9%) missing values Missing
lot5_numero has 3376821 (99.8%) missing values Missing
lot5_surface_carrez has 3382060 (> 99.9%) missing values Missing
code_type_local has 1508037 (44.6%) missing values Missing
type_local has 1508037 (44.6%) missing values Missing
surface_reelle_bati has 1992066 (58.9%) missing values Missing
nombre_pieces_principales has 1510700 (44.7%) missing values Missing
code_nature_culture has 1079606 (31.9%) missing values Missing
nature_culture has 1079606 (31.9%) missing values Missing
code_nature_culture_speciale has 3230429 (95.5%) missing values Missing
nature_culture_speciale has 3230429 (95.5%) missing values Missing
surface_terrain has 1079656 (31.9%) missing values Missing
longitude has 99869 (3.0%) missing values Missing
latitude has 99869 (3.0%) missing values Missing
numero_disposition is highly skewed (γ1 = 43.05693677) Skewed
lot1_surface_carrez is highly skewed (γ1 = 27.3471126) Skewed
lot2_surface_carrez is highly skewed (γ1 = 45.2560629) Skewed
lot4_numero is highly skewed (γ1 = 57.55405177) Skewed
lot5_numero is highly skewed (γ1 = 48.48277988) Skewed
nombre_lots is highly skewed (γ1 = 33.01288703) Skewed
surface_reelle_bati is highly skewed (γ1 = 305.1601927) Skewed
surface_terrain is highly skewed (γ1 = 25.42368337) Skewed
code_commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
numero_volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot1_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot2_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot3_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
nombre_lots has 2313128 (68.4%) zeros Zeros
nombre_pieces_principales has 603218 (17.8%) zeros Zeros

Reproduction

Analysis started2021-10-05 22:49:21.818662
Analysis finished2021-10-05 23:05:29.977323
Duration16 minutes and 8.16 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id_mutation
Categorical

HIGH CARDINALITY

Distinct1447220
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
2017-450452
 
5450
2017-1310773
 
4580
2017-1268155
 
4146
2017-1345691
 
3318
2017-1327546
 
2499
Other values (1447215)
3362819 

Length

Max length12
Median length11
Mean length11.20729647
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique698538 ?
Unique (%)20.6%

Sample

1st row2017-1
2nd row2017-2
3rd row2017-3
4th row2017-3
5th row2017-3

Common Values

ValueCountFrequency (%)
2017-4504525450
 
0.2%
2017-13107734580
 
0.1%
2017-12681554146
 
0.1%
2017-13456913318
 
0.1%
2017-13275462499
 
0.1%
2017-4435261872
 
0.1%
2017-8238691858
 
0.1%
2017-13458351756
 
0.1%
2017-13080691546
 
< 0.1%
2017-8238771428
 
< 0.1%
Other values (1447210)3354359
99.2%

Length

2021-10-06T01:05:30.184374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-4504525450
 
0.2%
2017-13107734580
 
0.1%
2017-12681554146
 
0.1%
2017-13456913318
 
0.1%
2017-13275462499
 
0.1%
2017-4435261872
 
0.1%
2017-8238691858
 
0.1%
2017-13458351756
 
0.1%
2017-13080691546
 
< 0.1%
2017-8238771428
 
< 0.1%
Other values (1447210)3354359
99.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_mutation
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
2017-04-27
 
59570
2017-12-29
 
38435
2017-12-21
 
34643
2017-12-28
 
32836
2017-12-22
 
32393
Other values (360)
3184935 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-02
2nd row2017-01-05
3rd row2017-01-06
4th row2017-01-06
5th row2017-01-06

Common Values

ValueCountFrequency (%)
2017-04-2759570
 
1.8%
2017-12-2938435
 
1.1%
2017-12-2134643
 
1.0%
2017-12-2832836
 
1.0%
2017-12-2232393
 
1.0%
2017-06-3029222
 
0.9%
2017-09-2925850
 
0.8%
2017-12-2024964
 
0.7%
2017-05-2324741
 
0.7%
2017-03-3124718
 
0.7%
Other values (355)3055440
90.3%

Length

2021-10-06T01:05:30.494953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-04-2759570
 
1.8%
2017-12-2938435
 
1.1%
2017-12-2134643
 
1.0%
2017-12-2832836
 
1.0%
2017-12-2232393
 
1.0%
2017-06-3029222
 
0.9%
2017-09-2925850
 
0.8%
2017-12-2024964
 
0.7%
2017-05-2324741
 
0.7%
2017-03-3124718
 
0.7%
Other values (355)3055440
90.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_disposition
Real number (ℝ≥0)

SKEWED

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.171767453
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:30.776017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum185
Range184
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.061452548
Coefficient of variation (CV)1.759267629
Kurtosis2567.344373
Mean1.171767453
Median Absolute Deviation (MAD)0
Skewness43.05693677
Sum3963869
Variance4.249586607
MonotonicityNot monotonic
2021-10-06T01:05:31.076598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13134086
92.6%
2195651
 
5.8%
329589
 
0.9%
45989
 
0.2%
84469
 
0.1%
52375
 
0.1%
61380
 
< 0.1%
281281
 
< 0.1%
7702
 
< 0.1%
32576
 
< 0.1%
Other values (173)6714
 
0.2%
ValueCountFrequency (%)
13134086
92.6%
2195651
 
5.8%
329589
 
0.9%
45989
 
0.2%
52375
 
0.1%
61380
 
< 0.1%
7702
 
< 0.1%
84469
 
0.1%
9279
 
< 0.1%
10336
 
< 0.1%
ValueCountFrequency (%)
1851
 
< 0.1%
1843
< 0.1%
1831
 
< 0.1%
1821
 
< 0.1%
1811
 
< 0.1%
1801
 
< 0.1%
1781
 
< 0.1%
1772
< 0.1%
1752
< 0.1%
1741
 
< 0.1%

nature_mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
Vente
3023253 
Vente en l'état futur d'achèvement
 
272048
Echange
 
46317
Vente terrain à bâtir
 
15037
Adjudication
 
14727

Length

Max length34
Median length5
Mean length7.488210104
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVente
2nd rowVente
3rd rowVente
4th rowVente
5th rowVente

Common Values

ValueCountFrequency (%)
Vente3023253
89.4%
Vente en l'état futur d'achèvement272048
 
8.0%
Echange46317
 
1.4%
Vente terrain à bâtir15037
 
0.4%
Adjudication14727
 
0.4%
Expropriation11430
 
0.3%

Length

2021-10-06T01:05:31.387668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:05:31.566216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
vente3310338
73.3%
d'achèvement272048
 
6.0%
futur272048
 
6.0%
l'état272048
 
6.0%
en272048
 
6.0%
echange46317
 
1.0%
bâtir15037
 
0.3%
à15037
 
0.3%
terrain15037
 
0.3%
adjudication14727
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valeur_fonciere
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct131008
Distinct (%)3.9%
Missing45338
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean1217839.376
Minimum0.01
Maximum686496000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:31.866288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2500
Q159000
median144462
Q3260000
95-th percentile1642412.1
Maximum686496000
Range686496000
Interquartile range (IQR)201000

Descriptive statistics

Standard deviation9447002.924
Coefficient of variation (CV)7.757183016
Kurtosis274.8009997
Mean1217839.376
Median Absolute Deviation (MAD)95538
Skewness14.50600536
Sum4.064507254 × 1012
Variance8.924586425 × 1013
MonotonicityNot monotonic
2021-10-06T01:05:32.168361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000030412
 
0.9%
15000028912
 
0.9%
12000028128
 
0.8%
125529
 
0.8%
8000024691
 
0.7%
5000024561
 
0.7%
20000023924
 
0.7%
13000023890
 
0.7%
9000023612
 
0.7%
6000023470
 
0.7%
Other values (130998)3080345
91.1%
(Missing)45338
 
1.3%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.023
 
< 0.1%
0.041
 
< 0.1%
0.14
 
< 0.1%
0.112
 
< 0.1%
0.122
 
< 0.1%
0.142
 
< 0.1%
0.15580
< 0.1%
0.1612
 
< 0.1%
0.18147
 
< 0.1%
ValueCountFrequency (%)
6864960001
 
< 0.1%
4897080001
 
< 0.1%
47678000027
< 0.1%
4450000006
 
< 0.1%
4020000003
 
< 0.1%
33531065617
 
< 0.1%
2955000001
 
< 0.1%
26224302414
 
< 0.1%
2187772642
 
< 0.1%
21654000059
< 0.1%

adresse_numero
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7162
Distinct (%)0.4%
Missing1395778
Missing (%)41.3%
Infinite0
Infinite (%)0.0%
Mean792.5980295
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:32.501436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median26
Q3100
95-th percentile5753
Maximum9999
Range9998
Interquartile range (IQR)92

Descriptive statistics

Standard deviation2142.359475
Coefficient of variation (CV)2.702958366
Kurtosis7.083597824
Mean792.5980295
Median Absolute Deviation (MAD)22
Skewness2.872950803
Sum1574919233
Variance4589704.12
MonotonicityNot monotonic
2021-10-06T01:05:32.842517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194674
 
2.8%
276718
 
2.3%
362536
 
1.8%
460341
 
1.8%
655192
 
1.6%
554850
 
1.6%
749049
 
1.4%
847924
 
1.4%
1044852
 
1.3%
941920
 
1.2%
Other values (7152)1398978
41.4%
(Missing)1395778
41.3%
ValueCountFrequency (%)
194674
2.8%
276718
2.3%
362536
1.8%
460341
1.8%
554850
1.6%
655192
1.6%
749049
1.4%
847924
1.4%
941920
1.2%
1044852
1.3%
ValueCountFrequency (%)
9999402
< 0.1%
999844
 
< 0.1%
999715
 
< 0.1%
999620
 
< 0.1%
999510
 
< 0.1%
999425
 
< 0.1%
99933
 
< 0.1%
99924
 
< 0.1%
999115
 
< 0.1%
999016
 
< 0.1%

adresse_suffixe
Categorical

HIGH CORRELATION
MISSING

Distinct41
Distinct (%)< 0.1%
Missing3237253
Missing (%)95.7%
Memory size25.8 MiB
B
83429 
A
22891 
F
12996 
T
11192 
C
 
4758
Other values (36)
10293 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B83429
 
2.5%
A22891
 
0.7%
F12996
 
0.4%
T11192
 
0.3%
C4758
 
0.1%
D2193
 
0.1%
E1253
 
< 0.1%
Q1250
 
< 0.1%
U1167
 
< 0.1%
P991
 
< 0.1%
Other values (31)3439
 
0.1%
(Missing)3237253
95.7%

Length

2021-10-06T01:05:33.340138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b83429
57.3%
a22891
 
15.7%
f12996
 
8.9%
t11192
 
7.7%
c4758
 
3.3%
d2193
 
1.5%
e1253
 
0.9%
q1250
 
0.9%
u1167
 
0.8%
p991
 
0.7%
Other values (27)3439
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_nom_voie
Categorical

HIGH CARDINALITY

Distinct478212
Distinct (%)14.3%
Missing30162
Missing (%)0.9%
Memory size25.8 MiB
LE VILLAGE
 
32081
LE BOURG
 
26888
RUE DE LA REPUBLIQUE
 
6740
RUE JEAN JAURES
 
6026
GR GRANDE RUE
 
5854
Other values (478207)
3275061 

Length

Max length31
Median length14
Mean length14.69918244
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162426 ?
Unique (%)4.8%

Sample

1st rowRUE CHARLES ROBIN
2nd rowLES VAVRES
3rd rowLA POIPE
4th rowLA POIPE
5th rowLA POIPE

Common Values

ValueCountFrequency (%)
LE VILLAGE32081
 
0.9%
LE BOURG26888
 
0.8%
RUE DE LA REPUBLIQUE6740
 
0.2%
RUE JEAN JAURES6026
 
0.2%
GR GRANDE RUE5854
 
0.2%
RUE PASTEUR5706
 
0.2%
RUE VICTOR HUGO5587
 
0.2%
AV JEAN JAURES5473
 
0.2%
RUE DE PARIS5357
 
0.2%
AV DE LA REPUBLIQUE4692
 
0.1%
Other values (478202)3248246
96.0%
(Missing)30162
 
0.9%

Length

2021-10-06T01:05:33.660723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue1118363
 
11.6%
de735850
 
7.6%
la495547
 
5.1%
du334491
 
3.5%
le286518
 
3.0%
des283200
 
2.9%
av270931
 
2.8%
les231098
 
2.4%
che107233
 
1.1%
rte101042
 
1.0%
Other values (200164)5665356
58.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_code_voie
Categorical

HIGH CARDINALITY

Distinct15865
Distinct (%)0.5%
Missing30113
Missing (%)0.9%
Memory size25.8 MiB
0020
 
17335
B005
 
15623
B004
 
15312
B008
 
15243
B002
 
15034
Other values (15860)
3274152 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1276 ?
Unique (%)< 0.1%

Sample

1st row0820
2nd rowB032
3rd rowB080
4th rowB080
5th rowB080

Common Values

ValueCountFrequency (%)
002017335
 
0.5%
B00515623
 
0.5%
B00415312
 
0.5%
B00815243
 
0.5%
B00215034
 
0.4%
B00314943
 
0.4%
B01014793
 
0.4%
B00714740
 
0.4%
B01314738
 
0.4%
B00614617
 
0.4%
Other values (15855)3200321
94.6%
(Missing)30113
 
0.9%

Length

2021-10-06T01:05:33.930809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
002017335
 
0.5%
b00515623
 
0.5%
b00415312
 
0.5%
b00815243
 
0.5%
b00215034
 
0.4%
b00314943
 
0.4%
b01014793
 
0.4%
b00714740
 
0.4%
b01314738
 
0.4%
b00614617
 
0.4%
Other values (15855)3200321
95.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_postal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5868
Distinct (%)0.2%
Missing30502
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean50696.88167
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:34.204899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6700
Q129630
median49400
Q375015
95-th percentile93140
Maximum97490
Range96490
Interquartile range (IQR)45385

Descriptive statistics

Standard deviation27399.42192
Coefficient of variation (CV)0.5404557642
Kurtosis-1.196477499
Mean50696.88167
Median Absolute Deviation (MAD)23750
Skewness-0.009140286181
Sum1.699516634 × 1011
Variance750728321.7
MonotonicityNot monotonic
2021-10-06T01:05:34.513982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
312008834
 
0.3%
350008373
 
0.2%
691007990
 
0.2%
210007768
 
0.2%
593007609
 
0.2%
540007088
 
0.2%
750156865
 
0.2%
750166681
 
0.2%
511006571
 
0.2%
292006318
 
0.2%
Other values (5858)3278213
96.9%
(Missing)30502
 
0.9%
ValueCountFrequency (%)
10001853
0.1%
1090399
 
< 0.1%
11001047
< 0.1%
1110599
 
< 0.1%
1120763
< 0.1%
1130467
 
< 0.1%
1140460
 
< 0.1%
11501115
< 0.1%
1160655
 
< 0.1%
11701435
< 0.1%
ValueCountFrequency (%)
974901785
0.1%
97480521
 
< 0.1%
97470241
 
< 0.1%
97460739
< 0.1%
97450185
 
< 0.1%
9744247
 
< 0.1%
97441217
 
< 0.1%
97440717
< 0.1%
9743949
 
< 0.1%
97438563
 
< 0.1%

code_commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size25.8 MiB

nom_commune
Categorical

HIGH CARDINALITY

Distinct30549
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
Toulouse
 
31001
Nantes
 
19016
Bordeaux
 
17624
Nice
 
17192
Montpellier
 
16912
Other values (30544)
3281067 

Length

Max length45
Median length10
Mean length11.84390885
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)< 0.1%

Sample

1st rowBourg-en-Bresse
2nd rowPéronnas
3rd rowSaint-Cyr-sur-Menthon
4th rowSaint-Cyr-sur-Menthon
5th rowSaint-Cyr-sur-Menthon

Common Values

ValueCountFrequency (%)
Toulouse31001
 
0.9%
Nantes19016
 
0.6%
Bordeaux17624
 
0.5%
Nice17192
 
0.5%
Montpellier16912
 
0.5%
Rennes13582
 
0.4%
Lille12653
 
0.4%
Villeurbanne8158
 
0.2%
Dijon7890
 
0.2%
Aix-en-Provence7826
 
0.2%
Other values (30539)3230958
95.5%

Length

2021-10-06T01:05:34.847578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement124918
 
3.2%
la102321
 
2.6%
le95086
 
2.4%
paris64921
 
1.7%
marseille35619
 
0.9%
les35379
 
0.9%
toulouse31001
 
0.8%
lyon24378
 
0.6%
nantes19016
 
0.5%
bordeaux17624
 
0.4%
Other values (30459)3375620
86.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size25.8 MiB

ancien_code_commune
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct787
Distinct (%)1.3%
Missing3323835
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean56822.89601
Minimum1025
Maximum95308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:35.232187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1025
5-th percentile9334
Q135100
median56165
Q380485
95-th percentile93070
Maximum95308
Range94283
Interquartile range (IQR)45385

Descriptive statistics

Standard deviation27504.53236
Coefficient of variation (CV)0.4840396088
Kurtosis-1.14627258
Mean56822.89601
Median Absolute Deviation (MAD)23164
Skewness-0.3339921404
Sum3351243938
Variance756499300.2
MonotonicityNot monotonic
2021-10-06T01:05:35.614273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
930702888
 
0.1%
851941919
 
0.1%
912281746
 
0.1%
851661479
 
< 0.1%
785511427
 
< 0.1%
953061021
 
< 0.1%
732571016
 
< 0.1%
85060917
 
< 0.1%
22050915
 
< 0.1%
78158695
 
< 0.1%
Other values (777)44954
 
1.3%
(Missing)3323835
98.3%
ValueCountFrequency (%)
1025200
< 0.1%
1033413
< 0.1%
103696
 
< 0.1%
105916
 
< 0.1%
1091255
< 0.1%
109745
 
< 0.1%
112251
 
< 0.1%
113063
 
< 0.1%
114453
 
< 0.1%
115437
 
< 0.1%
ValueCountFrequency (%)
9530827
 
< 0.1%
953061021
 
< 0.1%
9504031
 
< 0.1%
930702888
0.1%
91390213
 
< 0.1%
912281746
0.1%
9122212
 
< 0.1%
91182506
 
< 0.1%
900739
 
< 0.1%
9006839
 
< 0.1%

ancien_nom_commune
Categorical

HIGH CARDINALITY
MISSING

Distinct782
Distinct (%)1.3%
Missing3323835
Missing (%)98.3%
Memory size25.8 MiB
Saint-Ouen
 
2888
Les Sables-d'Olonne
 
1919
Évry
 
1746
Olonne-sur-Mer
 
1479
Saint-Germain-en-Laye
 
1427
Other values (777)
49518 

Length

Max length30
Median length10
Mean length12.52469607
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowCras-sur-Reyssouze
2nd rowÉtrez
3rd rowBâgé-la-Ville
4th rowCras-sur-Reyssouze
5th rowBâgé-la-Ville

Common Values

ValueCountFrequency (%)
Saint-Ouen2888
 
0.1%
Les Sables-d'Olonne1919
 
0.1%
Évry1746
 
0.1%
Olonne-sur-Mer1479
 
< 0.1%
Saint-Germain-en-Laye1427
 
< 0.1%
Herblay1021
 
< 0.1%
Les Belleville1016
 
< 0.1%
Château-d'Olonne917
 
< 0.1%
Dinan915
 
< 0.1%
Le Chesnay695
 
< 0.1%
Other values (772)44954
 
1.3%
(Missing)3323835
98.3%

Length

2021-10-06T01:05:35.936364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
les4805
 
6.8%
saint-ouen2888
 
4.1%
sables-d'olonne1919
 
2.7%
évry1746
 
2.5%
la1699
 
2.4%
belleville1494
 
2.1%
olonne-sur-mer1479
 
2.1%
saint-germain-en-laye1427
 
2.0%
le1280
 
1.8%
herblay1021
 
1.5%
Other values (791)50447
71.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id_parcelle
Categorical

HIGH CARDINALITY

Distinct2029416
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
91286000AR0114
 
3058
33193000AD0001
 
2812
29039000BL0050
 
1340
33193000AC0001
 
1287
33193000AA0002
 
1136
Other values (2029411)
3373179 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1619045 ?
Unique (%)47.9%

Sample

1st row01053000BK0039
2nd row01289000AR0388
3rd row01343000ZM0197
4th row01343000ZM0198
5th row01343000ZM0201

Common Values

ValueCountFrequency (%)
91286000AR01143058
 
0.1%
33193000AD00012812
 
0.1%
29039000BL00501340
 
< 0.1%
33193000AC00011287
 
< 0.1%
33193000AA00021136
 
< 0.1%
30189000EM0022828
 
< 0.1%
13056000AP0103816
 
< 0.1%
593830000B6722740
 
< 0.1%
93007000AO0314612
 
< 0.1%
47001000AI0309590
 
< 0.1%
Other values (2029406)3369593
99.6%

Length

2021-10-06T01:05:36.341968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91286000ar01143058
 
0.1%
33193000ad00012812
 
0.1%
29039000bl00501340
 
< 0.1%
33193000ac00011287
 
< 0.1%
33193000aa00021136
 
< 0.1%
30189000em0022828
 
< 0.1%
13056000ap0103816
 
< 0.1%
593830000b6722740
 
< 0.1%
93007000ao0314612
 
< 0.1%
47001000ai0309590
 
< 0.1%
Other values (2029406)3369593
99.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_id_parcelle
Categorical

HIGH CARDINALITY
MISSING

Distinct16409
Distinct (%)78.1%
Missing3361806
Missing (%)99.4%
Memory size25.8 MiB
85166000AC1258
 
152
91182000AB0141
 
112
85027000ZC0289
 
97
91182000AN0528
 
85
85166000AT0535
 
67
Other values (16404)
20493 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14024 ?
Unique (%)66.8%

Sample

1st row01154000ZC0118
2nd row01154000ZE0078
3rd row01154000ZH0020
4th row01154000ZH0020
5th row01154000ZH0022

Common Values

ValueCountFrequency (%)
85166000AC1258152
 
< 0.1%
91182000AB0141112
 
< 0.1%
85027000ZC028997
 
< 0.1%
91182000AN052885
 
< 0.1%
85166000AT053567
 
< 0.1%
85166000AC125962
 
< 0.1%
85166000AL032145
 
< 0.1%
782510000B025644
 
< 0.1%
85166000AT055741
 
< 0.1%
85166000AX009133
 
< 0.1%
Other values (16399)20268
 
0.6%
(Missing)3361806
99.4%

Length

2021-10-06T01:05:36.615067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
85166000ac1258152
 
0.7%
91182000ab0141112
 
0.5%
85027000zc028997
 
0.5%
91182000an052885
 
0.4%
85166000at053567
 
0.3%
85166000ac125962
 
0.3%
85166000al032145
 
0.2%
782510000b025644
 
0.2%
85166000at055741
 
0.2%
85166000ax009133
 
0.2%
Other values (16399)20268
96.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3373972
Missing (%)99.7%
Memory size25.8 MiB

lot1_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2313128
Missing (%)68.4%
Memory size25.8 MiB

lot1_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct18059
Distinct (%)6.5%
Missing3104314
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean68.57213215
Minimum0.36
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:36.890141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile16.7
Q134.0425
median53.6
Q373.68
95-th percentile118.3815
Maximum9999
Range9998.64
Interquartile range (IQR)39.6375

Descriptive statistics

Standard deviation217.985836
Coefficient of variation (CV)3.178927491
Kurtosis851.4324699
Mean68.57213215
Median Absolute Deviation (MAD)19.77
Skewness27.3471126
Sum19097201.66
Variance47517.82471
MonotonicityNot monotonic
2021-10-06T01:05:37.182214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5628
 
< 0.1%
12417
 
< 0.1%
15413
 
< 0.1%
47385
 
< 0.1%
10365
 
< 0.1%
30357
 
< 0.1%
40355
 
< 0.1%
60336
 
< 0.1%
20319
 
< 0.1%
42316
 
< 0.1%
Other values (18049)274607
 
8.1%
(Missing)3104314
91.8%
ValueCountFrequency (%)
0.365
 
< 0.1%
0.521
 
< 0.1%
0.571
 
< 0.1%
0.61
 
< 0.1%
0.851
 
< 0.1%
0.92
 
< 0.1%
0.971
 
< 0.1%
0.981
 
< 0.1%
0.991
 
< 0.1%
161
< 0.1%
ValueCountFrequency (%)
999913
< 0.1%
91181
 
< 0.1%
84091
 
< 0.1%
7992.21
 
< 0.1%
79121
 
< 0.1%
72641
 
< 0.1%
71011
 
< 0.1%
69391
 
< 0.1%
69331
 
< 0.1%
68871
 
< 0.1%

lot2_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3161194
Missing (%)93.4%
Memory size25.8 MiB

lot2_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct12325
Distinct (%)18.0%
Missing3314519
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean66.08874145
Minimum0.36
Maximum8928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:37.470939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile23.126
Q143.27
median61.11
Q376.45
95-th percentile111.32
Maximum8928
Range8927.64
Interquartile range (IQR)33.18

Descriptive statistics

Standard deviation130.3559422
Coefficient of variation (CV)1.972437958
Kurtosis2308.390612
Mean66.08874145
Median Absolute Deviation (MAD)16.7
Skewness45.2560629
Sum4513398.42
Variance16992.67168
MonotonicityNot monotonic
2021-10-06T01:05:37.786747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6880
 
< 0.1%
7073
 
< 0.1%
6069
 
< 0.1%
5569
 
< 0.1%
6369
 
< 0.1%
6568
 
< 0.1%
6765
 
< 0.1%
4063
 
< 0.1%
7560
 
< 0.1%
6458
 
< 0.1%
Other values (12315)67619
 
2.0%
(Missing)3314519
98.0%
ValueCountFrequency (%)
0.363
 
< 0.1%
0.51
 
< 0.1%
0.711
 
< 0.1%
0.971
 
< 0.1%
113
< 0.1%
1.051
 
< 0.1%
1.141
 
< 0.1%
1.21
 
< 0.1%
1.221
 
< 0.1%
1.242
 
< 0.1%
ValueCountFrequency (%)
89281
 
< 0.1%
85251
 
< 0.1%
79961
 
< 0.1%
79491
 
< 0.1%
72151
 
< 0.1%
71081
 
< 0.1%
64781
 
< 0.1%
5680.819
< 0.1%
46651
 
< 0.1%
43751
 
< 0.1%

lot3_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3346137
Missing (%)98.9%
Memory size25.8 MiB

lot3_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4689
Distinct (%)70.5%
Missing3376163
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean93.27204392
Minimum0.36
Maximum7529.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:38.139827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile12.5
Q137.67
median61.43
Q386.36
95-th percentile164.326
Maximum7529.1
Range7528.74
Interquartile range (IQR)48.69

Descriptive statistics

Standard deviation394.7234758
Coefficient of variation (CV)4.231959108
Kurtosis338.4473672
Mean93.27204392
Median Absolute Deviation (MAD)24.12
Skewness18.15884772
Sum620165.82
Variance155806.6223
MonotonicityNot monotonic
2021-10-06T01:05:38.582927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.527
 
< 0.1%
40125
 
< 0.1%
7.9820
 
< 0.1%
7529.118
 
< 0.1%
7013
 
< 0.1%
6713
 
< 0.1%
1013
 
< 0.1%
1213
 
< 0.1%
1512
 
< 0.1%
4312
 
< 0.1%
Other values (4679)6483
 
0.2%
(Missing)3376163
99.8%
ValueCountFrequency (%)
0.361
 
< 0.1%
0.381
 
< 0.1%
0.41
 
< 0.1%
0.521
 
< 0.1%
0.681
 
< 0.1%
12
< 0.1%
1.031
 
< 0.1%
1.211
 
< 0.1%
1.231
 
< 0.1%
1.53
< 0.1%
ValueCountFrequency (%)
7529.118
< 0.1%
1560.13
 
< 0.1%
1481.522
 
< 0.1%
1090.31
 
< 0.1%
1089.91
 
< 0.1%
1036.31
 
< 0.1%
8381
 
< 0.1%
7801
 
< 0.1%
756.71
 
< 0.1%
658.072
 
< 0.1%

lot4_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct814
Distinct (%)6.5%
Missing3370210
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean167.6030789
Minimum2
Maximum191612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:38.894143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median24
Q370.75
95-th percentile394.85
Maximum191612
Range191610
Interquartile range (IQR)62.75

Descriptive statistics

Standard deviation2699.381251
Coefficient of variation (CV)16.10579751
Kurtosis3621.121997
Mean167.6030789
Median Absolute Deviation (MAD)18
Skewness57.55405177
Sum2112134
Variance7286659.136
MonotonicityNot monotonic
2021-10-06T01:05:39.222217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7702
 
< 0.1%
9695
 
< 0.1%
8691
 
< 0.1%
6685
 
< 0.1%
4616
 
< 0.1%
5583
 
< 0.1%
3301
 
< 0.1%
2219
 
< 0.1%
13199
 
< 0.1%
18153
 
< 0.1%
Other values (804)7758
 
0.2%
(Missing)3370210
99.6%
ValueCountFrequency (%)
2219
 
< 0.1%
3301
< 0.1%
4616
< 0.1%
5583
< 0.1%
6685
< 0.1%
7702
< 0.1%
8691
< 0.1%
9695
< 0.1%
116
 
< 0.1%
12119
 
< 0.1%
ValueCountFrequency (%)
1916121
< 0.1%
1613111
< 0.1%
1412141
< 0.1%
530061
< 0.1%
300661
< 0.1%
250321
< 0.1%
250041
< 0.1%
230041
< 0.1%
200971
< 0.1%
200801
< 0.1%

lot4_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1444
Distinct (%)82.4%
Missing3381060
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean114.5579281
Minimum0.54
Maximum2321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:39.528799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile8.655
Q130.9325
median61.5
Q3100
95-th percentile291.415
Maximum2321
Range2320.46
Interquartile range (IQR)69.0675

Descriptive statistics

Standard deviation259.3426044
Coefficient of variation (CV)2.263855577
Kurtosis39.16993756
Mean114.5579281
Median Absolute Deviation (MAD)33.005
Skewness6.101077765
Sum200705.49
Variance67258.58644
MonotonicityNot monotonic
2021-10-06T01:05:39.805878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194525
 
< 0.1%
5.819
 
< 0.1%
25.39
 
< 0.1%
12.58
 
< 0.1%
128
 
< 0.1%
44.998
 
< 0.1%
86
 
< 0.1%
206
 
< 0.1%
156
 
< 0.1%
14.845
 
< 0.1%
Other values (1434)1652
 
< 0.1%
(Missing)3381060
99.9%
ValueCountFrequency (%)
0.541
< 0.1%
0.551
< 0.1%
0.871
< 0.1%
0.981
< 0.1%
12
< 0.1%
1.171
< 0.1%
1.311
< 0.1%
1.41
< 0.1%
1.581
< 0.1%
1.711
< 0.1%
ValueCountFrequency (%)
23211
 
< 0.1%
194525
< 0.1%
16121
 
< 0.1%
1560.13
 
< 0.1%
1481.521
 
< 0.1%
1333.71
 
< 0.1%
1243.522
 
< 0.1%
891.91
 
< 0.1%
743.281
 
< 0.1%
735.851
 
< 0.1%

lot5_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct584
Distinct (%)9.7%
Missing3376821
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean207.2927725
Minimum2
Maximum191613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:40.092456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median26
Q376
95-th percentile437.5
Maximum191613
Range191611
Interquartile range (IQR)68

Descriptive statistics

Standard deviation3399.215504
Coefficient of variation (CV)16.39813807
Kurtosis2531.539074
Mean207.2927725
Median Absolute Deviation (MAD)20
Skewness48.48277988
Sum1241891
Variance11554666.05
MonotonicityNot monotonic
2021-10-06T01:05:40.458047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8370
 
< 0.1%
9359
 
< 0.1%
7325
 
< 0.1%
5324
 
< 0.1%
6289
 
< 0.1%
4168
 
< 0.1%
14122
 
< 0.1%
3118
 
< 0.1%
290
 
< 0.1%
1582
 
< 0.1%
Other values (574)3744
 
0.1%
(Missing)3376821
99.8%
ValueCountFrequency (%)
290
 
< 0.1%
3118
 
< 0.1%
4168
< 0.1%
5324
< 0.1%
6289
< 0.1%
7325
< 0.1%
8370
< 0.1%
9359
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
1916131
< 0.1%
1613121
< 0.1%
530071
< 0.1%
250331
< 0.1%
250051
< 0.1%
200981
< 0.1%
200811
< 0.1%
200221
< 0.1%
120041
< 0.1%
100271
< 0.1%

lot5_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct630
Distinct (%)83.8%
Missing3382060
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean95.3599867
Minimum0.6
Maximum1560.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:40.783121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile6.944
Q124.7975
median62.355
Q3107.38
95-th percentile288.11
Maximum1560.1
Range1559.5
Interquartile range (IQR)82.5825

Descriptive statistics

Standard deviation144.6331889
Coefficient of variation (CV)1.516707309
Kurtosis47.52928584
Mean95.3599867
Median Absolute Deviation (MAD)41.24
Skewness5.824527235
Sum71710.71
Variance20918.75932
MonotonicityNot monotonic
2021-10-06T01:05:41.060183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6619
 
< 0.1%
26.98
 
< 0.1%
16.976
 
< 0.1%
21.445
 
< 0.1%
12.55
 
< 0.1%
38.785
 
< 0.1%
104
 
< 0.1%
45.84
 
< 0.1%
1560.13
 
< 0.1%
133
 
< 0.1%
Other values (620)690
 
< 0.1%
(Missing)3382060
> 99.9%
ValueCountFrequency (%)
0.61
< 0.1%
0.861
< 0.1%
1.22
< 0.1%
1.41
< 0.1%
1.671
< 0.1%
1.711
< 0.1%
2.931
< 0.1%
3.31
< 0.1%
3.791
< 0.1%
4.081
< 0.1%
ValueCountFrequency (%)
1560.13
< 0.1%
1047.71
 
< 0.1%
927.21
 
< 0.1%
884.881
 
< 0.1%
8051
 
< 0.1%
728.71
 
< 0.1%
658.071
 
< 0.1%
651.51
 
< 0.1%
643.331
 
< 0.1%
602.741
 
< 0.1%

nombre_lots
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.403809316
Minimum0
Maximum255
Zeros2313128
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:41.358250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum255
Range255
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8186193593
Coefficient of variation (CV)2.027242381
Kurtosis5522.619568
Mean0.403809316
Median Absolute Deviation (MAD)0
Skewness33.01288703
Sum1366011
Variance0.6701376555
MonotonicityNot monotonic
2021-10-06T01:05:41.685838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02313128
68.4%
1848066
 
25.1%
2184943
 
5.5%
324073
 
0.7%
46611
 
0.2%
52389
 
0.1%
61254
 
< 0.1%
7631
 
< 0.1%
8468
 
< 0.1%
9278
 
< 0.1%
Other values (72)971
 
< 0.1%
ValueCountFrequency (%)
02313128
68.4%
1848066
 
25.1%
2184943
 
5.5%
324073
 
0.7%
46611
 
0.2%
52389
 
0.1%
61254
 
< 0.1%
7631
 
< 0.1%
8468
 
< 0.1%
9278
 
< 0.1%
ValueCountFrequency (%)
2551
< 0.1%
1841
< 0.1%
1371
< 0.1%
1261
< 0.1%
1181
< 0.1%
1171
< 0.1%
1161
< 0.1%
1121
< 0.1%
1081
< 0.1%
1051
< 0.1%

code_type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1508037
Missing (%)44.6%
Memory size25.8 MiB
1.0
660584 
2.0
611108 
3.0
475844 
4.0
127239 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0660584
19.5%
2.0611108
18.1%
3.0475844
 
14.1%
4.0127239
 
3.8%
(Missing)1508037
44.6%

Length

2021-10-06T01:05:42.044919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:05:42.219958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0660584
35.2%
2.0611108
32.6%
3.0475844
25.4%
4.0127239
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1508037
Missing (%)44.6%
Memory size25.8 MiB
Maison
660584 
Appartement
611108 
Dépendance
475844 
Local industriel. commercial ou assimilé
127239 

Length

Max length40
Median length10
Mean length10.95261671
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppartement
2nd rowDépendance
3rd rowAppartement
4th rowAppartement
5th rowAppartement

Common Values

ValueCountFrequency (%)
Maison660584
19.5%
Appartement611108
18.1%
Dépendance475844
 
14.1%
Local industriel. commercial ou assimilé127239
 
3.8%
(Missing)1508037
44.6%

Length

2021-10-06T01:05:42.542544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:05:42.796891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
maison660584
27.7%
appartement611108
25.6%
dépendance475844
20.0%
assimilé127239
 
5.3%
ou127239
 
5.3%
commercial127239
 
5.3%
industriel127239
 
5.3%
local127239
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_reelle_bati
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4596
Distinct (%)0.3%
Missing1992066
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean117.7884474
Minimum1
Maximum646230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:43.251643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q149
median74
Q3103
95-th percentile186
Maximum646230
Range646229
Interquartile range (IQR)54

Descriptive statistics

Standard deviation1747.355457
Coefficient of variation (CV)14.83469301
Kurtosis109304.3218
Mean117.7884474
Median Absolute Deviation (MAD)26
Skewness305.1601927
Sum163813812
Variance3053251.092
MonotonicityNot monotonic
2021-10-06T01:05:43.524731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8026855
 
0.8%
6025802
 
0.8%
7024171
 
0.7%
9023160
 
0.7%
5020512
 
0.6%
10020205
 
0.6%
6520178
 
0.6%
4019196
 
0.6%
4517780
 
0.5%
7516553
 
0.5%
Other values (4586)1176334
34.8%
(Missing)1992066
58.9%
ValueCountFrequency (%)
1338
 
< 0.1%
2344
 
< 0.1%
3342
 
< 0.1%
4183
 
< 0.1%
5381
 
< 0.1%
6390
 
< 0.1%
7360
 
< 0.1%
8717
 
< 0.1%
91066
 
< 0.1%
102907
0.1%
ValueCountFrequency (%)
6462308
< 0.1%
3171731
 
< 0.1%
2726001
 
< 0.1%
2121202
 
< 0.1%
1668011
 
< 0.1%
1371821
 
< 0.1%
1322331
 
< 0.1%
1210311
 
< 0.1%
1200001
 
< 0.1%
1147421
 
< 0.1%

nombre_pieces_principales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct54
Distinct (%)< 0.1%
Missing1510700
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean2.340234452
Minimum0
Maximum112
Zeros603218
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:43.842391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum112
Range112
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.075923995
Coefficient of variation (CV)0.8870581295
Kurtosis11.96713614
Mean2.340234452
Median Absolute Deviation (MAD)2
Skewness0.8435610494
Sum4381181
Variance4.309460435
MonotonicityNot monotonic
2021-10-06T01:05:44.261586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0603218
 
17.8%
3317013
 
9.4%
4308742
 
9.1%
2228614
 
6.8%
5177740
 
5.3%
1128653
 
3.8%
667586
 
2.0%
725042
 
0.7%
89011
 
0.3%
93331
 
0.1%
Other values (44)3162
 
0.1%
(Missing)1510700
44.7%
ValueCountFrequency (%)
0603218
17.8%
1128653
 
3.8%
2228614
 
6.8%
3317013
9.4%
4308742
9.1%
5177740
 
5.3%
667586
 
2.0%
725042
 
0.7%
89011
 
0.3%
93331
 
0.1%
ValueCountFrequency (%)
1121
< 0.1%
931
< 0.1%
781
< 0.1%
701
< 0.1%
681
< 0.1%
661
< 0.1%
651
< 0.1%
601
< 0.1%
581
< 0.1%
551
< 0.1%

code_nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1079606
Missing (%)31.9%
Memory size25.8 MiB
S
1078512 
T
336587 
P
176385 
AB
155548 
J
116100 
Other values (22)
440074 

Length

Max length2
Median length1
Mean length1.209177121
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAB
2nd rowP
3rd rowP
4th rowP
5th rowP

Common Values

ValueCountFrequency (%)
S1078512
31.9%
T336587
 
9.9%
P176385
 
5.2%
AB155548
 
4.6%
J116100
 
3.4%
L91531
 
2.7%
BT91324
 
2.7%
AG80456
 
2.4%
VI40718
 
1.2%
BR34074
 
1.0%
Other values (17)101971
 
3.0%
(Missing)1079606
31.9%

Length

2021-10-06T01:05:44.568656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s1078512
46.8%
t336587
 
14.6%
p176385
 
7.7%
ab155548
 
6.8%
j116100
 
5.0%
l91531
 
4.0%
bt91324
 
4.0%
ag80456
 
3.5%
vi40718
 
1.8%
br34074
 
1.5%
Other values (17)101971
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1079606
Missing (%)31.9%
Memory size25.8 MiB
sols
1078512 
terres
336587 
prés
176385 
terrains a bâtir
155548 
jardins
116100 
Other values (22)
440074 

Length

Max length19
Median length4
Mean length6.774368424
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowterrains a bâtir
2nd rowprés
3rd rowprés
4th rowprés
5th rowprés

Common Values

ValueCountFrequency (%)
sols1078512
31.9%
terres336587
 
9.9%
prés176385
 
5.2%
terrains a bâtir155548
 
4.6%
jardins116100
 
3.4%
landes91531
 
2.7%
taillis simples91324
 
2.7%
terrains d'agrément80456
 
2.4%
vignes40718
 
1.2%
futaies résineuses34074
 
1.0%
Other values (17)101971
 
3.0%
(Missing)1079606
31.9%

Length

2021-10-06T01:05:44.844231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sols1078512
37.6%
terres336682
 
11.8%
terrains236004
 
8.2%
prés178998
 
6.2%
a155548
 
5.4%
bâtir155548
 
5.4%
jardins116100
 
4.1%
taillis107842
 
3.8%
landes91834
 
3.2%
simples91324
 
3.2%
Other values (24)316392
 
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct125
Distinct (%)0.1%
Missing3230429
Missing (%)95.5%
Memory size25.8 MiB
POTAG
32312 
PATUR
15859 
PIN
12896 
PARC
12788 
FRICH
10053 
Other values (120)
68475 

Length

Max length5
Median length5
Mean length4.484279742
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowIMM
2nd rowIMM
3rd rowIMM
4th rowIMM
5th rowIMM

Common Values

ValueCountFrequency (%)
POTAG32312
 
1.0%
PATUR15859
 
0.5%
PIN12896
 
0.4%
PARC12788
 
0.4%
FRICH10053
 
0.3%
VAOC7562
 
0.2%
CHAT4801
 
0.1%
IMM3866
 
0.1%
MARAI3475
 
0.1%
CHENE3230
 
0.1%
Other values (115)45541
 
1.3%
(Missing)3230429
95.5%

Length

2021-10-06T01:05:45.179335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
potag32312
21.2%
patur15859
 
10.4%
pin12896
 
8.5%
parc12788
 
8.4%
frich10053
 
6.6%
vaoc7562
 
5.0%
chat4801
 
3.2%
imm3866
 
2.5%
marai3475
 
2.3%
chene3230
 
2.1%
Other values (115)45541
29.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct125
Distinct (%)0.1%
Missing3230429
Missing (%)95.5%
Memory size25.8 MiB
Jardin potager
32312 
Pâture plantée
15859 
Pins
12896 
Parc
12788 
Friche
10053 
Other values (120)
68475 

Length

Max length38
Median length14
Mean length12.54715421
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowDépendances d'ensemble immobilier
2nd rowDépendances d'ensemble immobilier
3rd rowDépendances d'ensemble immobilier
4th rowDépendances d'ensemble immobilier
5th rowDépendances d'ensemble immobilier

Common Values

ValueCountFrequency (%)
Jardin potager32312
 
1.0%
Pâture plantée15859
 
0.5%
Pins12896
 
0.4%
Parc12788
 
0.4%
Friche10053
 
0.3%
Vins d'appellation d'origine contrôlée7562
 
0.2%
Châtaigneraie4801
 
0.1%
Dépendances d'ensemble immobilier3866
 
0.1%
Pré marais3475
 
0.1%
Chênes3230
 
0.1%
Other values (115)45541
 
1.3%
(Missing)3230429
95.5%

Length

2021-10-06T01:05:45.539430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardin33856
 
12.2%
potager32312
 
11.7%
pâture15859
 
5.7%
plantée15859
 
5.7%
pins12896
 
4.7%
parc12792
 
4.6%
friche10053
 
3.6%
ou7784
 
2.8%
vins7718
 
2.8%
d'origine7562
 
2.7%
Other values (159)120312
43.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_terrain
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct46226
Distinct (%)2.0%
Missing1079656
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean4134.806089
Minimum1
Maximum4620522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 MiB
2021-10-06T01:05:45.818520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1237
median624
Q31922
95-th percentile13312
Maximum4620522
Range4620521
Interquartile range (IQR)1685

Descriptive statistics

Standard deviation24181.58361
Coefficient of variation (CV)5.848299314
Kurtosis1653.967431
Mean4134.806089
Median Absolute Deviation (MAD)499
Skewness25.42368337
Sum9523103453
Variance584748986.1
MonotonicityNot monotonic
2021-10-06T01:05:46.124102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50044352
 
1.3%
100021616
 
0.6%
8006696
 
0.2%
6006519
 
0.2%
126029
 
0.2%
4005559
 
0.2%
135430
 
0.2%
7005308
 
0.2%
20005168
 
0.2%
2005125
 
0.2%
Other values (46216)2191354
64.8%
(Missing)1079656
31.9%
ValueCountFrequency (%)
14983
0.1%
23976
0.1%
33726
0.1%
43814
0.1%
53977
0.1%
63829
0.1%
73552
0.1%
83731
0.1%
93486
0.1%
104469
0.1%
ValueCountFrequency (%)
46205221
< 0.1%
30585251
< 0.1%
30410181
< 0.1%
26365761
< 0.1%
26330472
< 0.1%
26041571
< 0.1%
24794581
< 0.1%
24453452
< 0.1%
18334301
< 0.1%
17549531
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1774859
Distinct (%)54.1%
Missing99869
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2.264622777
Minimum-63.152364
Maximum55.828599
Zeros0
Zeros (%)0.0%
Negative756135
Negative (%)22.4%
Memory size25.8 MiB
2021-10-06T01:05:46.408166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-63.152364
5-th percentile-2.210359
Q10.2140255
median2.343244
Q34.500259
95-th percentile6.582639
Maximum55.828599
Range118.980963
Interquartile range (IQR)4.2862335

Descriptive statistics

Standard deviation6.096855988
Coefficient of variation (CV)2.692217022
Kurtosis70.80281769
Mean2.264622777
Median Absolute Deviation (MAD)2.136624
Skewness-1.462984273
Sum7434627.493
Variance37.17165293
MonotonicityNot monotonic
2021-10-06T01:05:46.692230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.1516642812
 
0.1%
-3.9172731340
 
< 0.1%
-1.1338831287
 
< 0.1%
-1.1485461137
 
< 0.1%
4.33429828
 
< 0.1%
5.039083816
 
< 0.1%
2.475669612
 
< 0.1%
0.628714590
 
< 0.1%
2.285987568
 
< 0.1%
6.015076568
 
< 0.1%
Other values (1774849)3272385
96.7%
(Missing)99869
 
3.0%
ValueCountFrequency (%)
-63.1523641
 
< 0.1%
-63.1381251
 
< 0.1%
-63.1380272
 
< 0.1%
-63.1313982
 
< 0.1%
-63.1289024
 
< 0.1%
-63.1150447
 
< 0.1%
-63.1149777
 
< 0.1%
-63.1143941
 
< 0.1%
-63.1123471
 
< 0.1%
-63.1115223
< 0.1%
ValueCountFrequency (%)
55.8285991
 
< 0.1%
55.8273824
< 0.1%
55.8254821
 
< 0.1%
55.8253131
 
< 0.1%
55.8242231
 
< 0.1%
55.8242071
 
< 0.1%
55.8241821
 
< 0.1%
55.8226331
 
< 0.1%
55.8220181
 
< 0.1%
55.8206681
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1717835
Distinct (%)52.3%
Missing99869
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean46.17557609
Minimum-21.385366
Maximum51.082118
Zeros0
Zeros (%)0.0%
Negative15371
Negative (%)0.5%
Memory size25.8 MiB
2021-10-06T01:05:46.998299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-21.385366
5-th percentile43.2229215
Q144.701166
median46.727986
Q348.70185
95-th percentile49.921988
Maximum51.082118
Range72.467484
Interquartile range (IQR)4.000684

Descriptive statistics

Standard deviation5.577619068
Coefficient of variation (CV)0.1207915426
Kurtosis102.0080856
Mean46.17557609
Median Absolute Deviation (MAD)1.986675
Skewness-9.126972346
Sum151591784.3
Variance31.10983446
MonotonicityNot monotonic
2021-10-06T01:05:47.247863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.4124122812
 
0.1%
47.8825781340
 
< 0.1%
45.4137091287
 
< 0.1%
45.4174611136
 
< 0.1%
43.823295828
 
< 0.1%
43.408129816
 
< 0.1%
48.926664612
 
< 0.1%
44.208483590
 
< 0.1%
48.813074569
 
< 0.1%
43.714376565
 
< 0.1%
Other values (1717825)3272388
96.7%
(Missing)99869
 
3.0%
ValueCountFrequency (%)
-21.3853661
 
< 0.1%
-21.3853371
 
< 0.1%
-21.3846881
 
< 0.1%
-21.3846691
 
< 0.1%
-21.3845991
 
< 0.1%
-21.384581
 
< 0.1%
-21.3843152
< 0.1%
-21.3843141
 
< 0.1%
-21.3841791
 
< 0.1%
-21.3840344
< 0.1%
ValueCountFrequency (%)
51.0821183
< 0.1%
51.0820452
 
< 0.1%
51.0819476
< 0.1%
51.0817655
< 0.1%
51.081712
 
< 0.1%
51.0816782
 
< 0.1%
51.0815763
< 0.1%
51.0813753
< 0.1%
51.0811023
< 0.1%
51.0808721
 
< 0.1%

Interactions

2021-10-06T01:03:11.828467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:37.321873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:03.082692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:29.659838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:52.300351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:17.352805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:24.754487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:34.115805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:41.354908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:48.664613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:56.236124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:03.018247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:10.468784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:19.739908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:44.105114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:02.069139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:22.256855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:43.860017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:14.678924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:39.814481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:05.828706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:31.502728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:55.097838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:17.808297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:25.389628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:34.552905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:41.754110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:49.054771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:56.632128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:03.415352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:10.906882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:22.313062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:45.554096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:03.862235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:24.265373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:46.662462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:16.616639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:41.427380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:07.743269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:33.415473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:56.947747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:18.208405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:26.068294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:34.942992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:42.161714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:49.463666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:56.988222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:03.837448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:11.309480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:24.073951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:46.955601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:05.638751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:25.381412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:48.459421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:19.396989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:43.892934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:10.553889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:35.345556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:59.764425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:18.614016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:26.729861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:35.346855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:42.586808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:49.862759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:57.350321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:04.227536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:11.691353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:26.858414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:48.376699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:07.327537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:27.430990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:51.193185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:19.877119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:44.388046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:11.014533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:00:47.486515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:54.774633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:01:09.261974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:16.534724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:37.577683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:57.371316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:16.810506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:03:02.804989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:33.975606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:58:57.365774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:24.942624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:47.571868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:01:40.155053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:02:18.520516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:58:59.862659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:27.776074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:59:49.493662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:16.936708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:00:33.652623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:40.961812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:48.252194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:00:55.576826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:01:02.613158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:01:42.693560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:00.173364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:20.196893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:02:41.023015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:03:08.670155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-10-06T01:05:47.554933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-06T01:05:48.563649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-06T01:05:49.340828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-06T01:05:50.160022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-06T01:05:50.581117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-06T01:03:43.911951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-06T01:04:02.948771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-06T01:04:58.086289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-06T01:05:11.250738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
02017-12017-01-021Vente27000.083.0NaNRUE CHARLES ROBIN08201000.01053Bourg-en-Bresse1NaNNaN01053000BK0039NaNNaN13NaN834.24NaNNaNNaNNaNNaNNaN22.0Appartement37.02.0NaNNaNNaNNaNNaN5.23442046.206151
12017-22017-01-051Vente115000.0NaNNaNLES VAVRESB0321960.01289Péronnas1NaNNaN01289000AR0388NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNABterrains a bâtirNaNNaN788.05.20342546.176104
22017-32017-01-061Vente1.0NaNNaNLA POIPEB0801380.01343Saint-Cyr-sur-Menthon1NaNNaN01343000ZM0197NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNPprésNaNNaN42.04.97585446.277352
32017-32017-01-061Vente1.0NaNNaNLA POIPEB0801380.01343Saint-Cyr-sur-Menthon1NaNNaN01343000ZM0198NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNPprésNaNNaN77.04.97561646.277595
42017-32017-01-061Vente1.0NaNNaNLA POIPEB0801380.01343Saint-Cyr-sur-Menthon1NaNNaN01343000ZM0201NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNPprésNaNNaN94.04.97546446.276790
52017-42017-01-091Vente1.0NaNNaNMONTGRIMOUX CENTREB2051570.01159Feillens1NaNNaN01159000AH0996NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNPprésNaNNaN50.04.90405846.337125
62017-52017-01-031Vente258000.011.0NaNIMP DES PINSONS03841000.01344Saint-Denis-lès-Bourg1NaNNaN01344000AK0042NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN03.0DépendanceNaN0.0SsolsNaNNaN655.05.20575946.197471
72017-52017-01-031Vente258000.011.0NaNIMP DES PINSONS03841000.01344Saint-Denis-lès-Bourg1NaNNaN01344000AK0042NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0Appartement22.01.0SsolsNaNNaN655.05.20575946.197471
82017-52017-01-031Vente258000.011.0NaNIMP DES PINSONS03841000.01344Saint-Denis-lès-Bourg1NaNNaN01344000AK0042NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0Appartement22.01.0SsolsNaNNaN655.05.20575946.197471
92017-52017-01-031Vente258000.011.0NaNIMP DES PINSONS03841000.01344Saint-Denis-lès-Bourg1NaNNaN01344000AK0042NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0Appartement120.05.0SsolsNaNNaN655.05.20575946.197471

Last rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
33828022017-14472132017-12-211Vente571000.094.0NaNRUE D ABOUKIR004075002.075102Paris 2e Arrondissement75NaNNaN75102000AN0137NaNNaN2471.456NaN7NaNNaNNaNNaNNaN32.0Appartement90.03.0NaNNaNNaNNaNNaN2.34808648.867918
33828032017-14472142017-12-151Vente1246493.014.0NaNRUE SAINT ANTOINE849075004.075104Paris 4e Arrondissement75NaNNaN75104000AO0062NaNNaN4390.61NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement80.04.0NaNNaNNaNNaNNaN2.36658548.853693
33828042017-14472152017-11-231Vente428700.09.0NaNRUE SAINT MERRI870375004.075104Paris 4e Arrondissement75NaNNaN75104000AF0057NaNNaN11NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN2.35305948.859215
33828052017-14472152017-11-231Vente428700.09.0NaNRUE SAINT MERRI870375004.075104Paris 4e Arrondissement75NaNNaN75104000AF0057NaNNaN1233.32NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement36.02.0NaNNaNNaNNaNNaN2.35305948.859215
33828062017-14472162017-10-171Vente120000.010.0NaNRUE SAINT MARC868675002.075102Paris 2e Arrondissement75NaNNaN75102000AG0062NaNNaN104110.00NaNNaNNaNNaNNaNNaNNaNNaN14.0Local industriel. commercial ou assimilé109.00.0NaNNaNNaNNaNNaN2.34150548.870498
33828072017-14472172017-12-201Vente920000.03.0NaNRUE SAINT JOSEPH865975002.075102Paris 2e Arrondissement75NaNNaN75102000AI0034NaNNaN24NaN32NaN591.83NaNNaNNaNNaN34.0Local industriel. commercial ou assimilé90.00.0NaNNaNNaNNaNNaN2.34512248.868199
33828082017-14472182017-10-161Vente325000.012.0NaNQUAI DES CELESTINS163675004.075104Paris 4e Arrondissement75NaNNaN75104000AQ0069NaNNaN145.07NaNNaNNaNNaNNaNNaNNaNNaN14.0Local industriel. commercial ou assimilé55.00.0NaNNaNNaNNaNNaN2.36147648.852214
33828092017-14472182017-10-161Vente325000.012.0NaNQUAI DES CELESTINS163675004.075104Paris 4e Arrondissement75NaNNaN75104000AQ0069NaNNaN518.49NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement19.01.0NaNNaNNaNNaNNaN2.36147648.852214
33828102017-14472192017-12-081Vente255000.01.0NaNRUE PAUL LELONG716775002.075102Paris 2e Arrondissement75NaNNaN75102000AJ0151NaNNaN1522.11NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement26.02.0NaNNaNNaNNaNNaN2.34361948.867706
33828112017-14472202017-12-061Vente676000.053.0NaNRUE DU TEMPLE919175004.075104Paris 4e Arrondissement75NaNNaN75104000AG0031NaNNaN10260.00201NaNNaNNaNNaNNaNNaNNaN24.0Local industriel. commercial ou assimilé62.00.0NaNNaNNaNNaNNaN2.35424348.860198